Upload CryptoGAT dataset files

hf_cryptogat_crypto_1d/README.md

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+---
+license: other
+pretty_name: CryptoGAT Cryptocurrency Daily OHLCV Dataset
+task_categories:
+- time-series-forecasting
+language:
+- en
+size_categories:
+- 10K<n<100K
+tags:
+- cryptocurrency
+- crypto
+- finance
+- quantitative-finance
+- ai-for-finance
+- time-series
+- graph-neural-networks
+- graph-attention-network
+- cross-asset-modeling
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/raw_ohlcv.csv
+---
+
+# CryptoGAT Cryptocurrency Daily OHLCV Dataset
+
+This dataset accompanies the paper **CryptoGAT: Are Time Series Models Effective for Cryptocurrency Forecasting?** by Yu Peng, Matloob Khushi, and Josiah Poon.
+
+- Paper: https://arxiv.org/abs/2606.27670
+- Code: https://github.com/FanBroWell/CryptoGAT
+
+## Dataset Summary
+
+The repository contains daily OHLCV cryptocurrency market data and the processed tensors used by CryptoGAT.
+
+- Raw OHLCV file: `data/raw_ohlcv.csv`
+- Raw rows: 68,000
+- Raw symbols: 68 USDT trading pairs
+- Model assets: 66 cryptocurrencies
+- Common model window: 999 daily observations
+- Excluded from processed model tensors: `USDCUSDT`, `TUSDUSDT`
+
+The default Hugging Face dataset loads the long-format raw OHLCV table:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("CharlieYPeng/cryptogat-crypto-1d")
+print(dataset["train"][0])
+```
+
+## Files
+
+```text
+data/raw_ohlcv.csv
+processed/CRYPTO_1D_ALL/eod_data.pkl
+processed/CRYPTO_1D_ALL/price_data.pkl
+processed/CRYPTO_1D_ALL/gt_data.pkl
+processed/CRYPTO_1D_ALL/mask_data.pkl
+processed/CRYPTO_1D_ALL/coin_names.txt
+processed/CRYPTO_1D_ENHANCED/eod_data.pkl
+processed/CRYPTO_1D_ENHANCED/price_data.pkl
+processed/CRYPTO_1D_ENHANCED/gt_data.pkl
+processed/CRYPTO_1D_ENHANCED/mask_data.pkl
+processed/CRYPTO_1D_ENHANCED/coin_names.txt
+metadata/base_feature_names.csv
+metadata/enhanced_feature_names.csv
+metadata/cryptogat_model_assets.csv
+metadata/manifest.json
+```
+
+## Column Description
+
+`data/raw_ohlcv.csv` contains:
+
+- `symbol`: trading pair symbol, e.g. `BTCUSDT`
+- `base_asset`: base cryptocurrency ticker, e.g. `BTC`
+- `quote_asset`: quote asset, fixed as `USDT`
+- `date`: daily timestamp
+- `open`, `high`, `low`, `close`, `volume`: OHLCV market fields
+- `included_in_cryptogat`: whether this symbol is included in the processed CryptoGAT tensors
+- `source_file`: source CSV filename in the original GitHub repository
+
+## Processed Tensor Layout
+
+The processed files are Python pickle files containing NumPy arrays.
+
+### `CRYPTO_1D_ALL`
+
+- `eod_data.pkl`: shape `(66, 999, 5)`, normalized OHLCV-derived features
+- `price_data.pkl`: shape `(66, 999)`, close prices
+- `gt_data.pkl`: shape `(66, 999)`, next-period return labels
+- `mask_data.pkl`: shape `(66, 999)`, valid-observation mask
+- `coin_names.txt`: asset order for axis 0
+
+Feature names are listed in `metadata/base_feature_names.csv`.
+
+### `CRYPTO_1D_ENHANCED`
+
+- `eod_data.pkl`: shape `(66, 999, 35)`, base features plus technical indicators
+- `price_data.pkl`: shape `(66, 999)`, close prices
+- `gt_data.pkl`: shape `(66, 999)`, next-period return labels
+- `mask_data.pkl`: shape `(66, 999)`, valid-observation mask
+- `coin_names.txt`: asset order for axis 0
+
+Feature names are listed in `metadata/enhanced_feature_names.csv`.
+
+## Reproducing CryptoGAT
+
+The original training code expects the processed tensors under the GitHub repository's `dataset/` directory. To use this Hugging Face copy for reproduction, download the `processed/` folders and place them as:
+
+```text
+CryptoGAT/dataset/CRYPTO_1D_ALL/
+CryptoGAT/dataset/CRYPTO_1D_ENHANCED/
+```
+
+Then follow the training instructions in the GitHub repository.
+
+## Data Source and License Note
+
+The raw market data are cryptocurrency OHLCV records collected from Binance USDT trading pairs and released here for academic research and reproducibility of the CryptoGAT experiments. Users should independently verify that their intended use complies with the terms of the original data source.
+
+## Citation
+
+```bibtex
+@misc{peng2026cryptogat,
+  title = {{CryptoGAT}: Are Time Series Models Effective for Cryptocurrency Forecasting?},
+  author = {Peng, Yu and Khushi, Matloob and Poon, Josiah},
+  year = {2026},
+  eprint = {2606.27670},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.CE},
+  doi = {10.48550/arXiv.2606.27670},
+  url = {https://arxiv.org/abs/2606.27670}
+}
+```

hf_cryptogat_crypto_1d/metadata/base_feature_names.csv

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+feature_index,feature_name,dataset
+0,open_norm,CRYPTO_1D_ALL
+1,high_norm,CRYPTO_1D_ALL
+2,low_norm,CRYPTO_1D_ALL
+3,close_norm,CRYPTO_1D_ALL
+4,volume_norm,CRYPTO_1D_ALL

hf_cryptogat_crypto_1d/metadata/cryptogat_model_assets.csv

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+axis_0_index,symbol,base_asset
+0,1INCHUSDT,1INCH
+1,AAVEUSDT,AAVE
+2,ADAUSDT,ADA
+3,ALGOUSDT,ALGO
+4,APTUSDT,APT
+5,ARBUSDT,ARB
+6,ARUSDT,AR
+7,ATOMUSDT,ATOM
+8,AVAXUSDT,AVAX
+9,BATUSDT,BAT
+10,BCHUSDT,BCH
+11,BNBUSDT,BNB
+12,BTCUSDT,BTC
+13,CAKEUSDT,CAKE
+14,CFXUSDT,CFX
+15,CHZUSDT,CHZ
+16,COMPUSDT,COMP
+17,CRVUSDT,CRV
+18,DASHUSDT,DASH
+19,DCRUSDT,DCR
+20,DOGEUSDT,DOGE
+21,DOTUSDT,DOT
+22,ENSUSDT,ENS
+23,ETCUSDT,ETC
+24,ETHUSDT,ETH
+25,FETUSDT,FET
+26,FILUSDT,FIL
+27,GALAUSDT,GALA
+28,GLMUSDT,GLM
+29,GNOUSDT,GNO
+30,GRTUSDT,GRT
+31,HBARUSDT,HBAR
+32,ICPUSDT,ICP
+33,IMXUSDT,IMX
+34,INJUSDT,INJ
+35,IOTAUSDT,IOTA
+36,JASMYUSDT,JASMY
+37,JSTUSDT,JST
+38,LDOUSDT,LDO
+39,LINKUSDT,LINK
+40,LTCUSDT,LTC
+41,LUNCUSDT,LUNC
+42,MANAUSDT,MANA
+43,NEARUSDT,NEAR
+44,NEOUSDT,NEO
+45,NEXOUSDT,NEXO
+46,OPUSDT,OP
+47,PAXGUSDT,PAXG
+48,QNTUSDT,QNT
+49,RAYUSDT,RAY
+50,RUNEUSDT,RUNE
+51,SANDUSDT,SAND
+52,SHIBUSDT,SHIB
+53,SOLUSDT,SOL
+54,STXUSDT,STX
+55,SUNUSDT,SUN
+56,THETAUSDT,THETA
+57,TRXUSDT,TRX
+58,TWTUSDT,TWT
+59,UNIUSDT,UNI
+60,VETUSDT,VET
+61,XECUSDT,XEC
+62,XLMUSDT,XLM
+63,XRPUSDT,XRP
+64,XTZUSDT,XTZ
+65,ZECUSDT,ZEC

hf_cryptogat_crypto_1d/metadata/enhanced_feature_names.csv

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+feature_index,feature_name,dataset
+0,open_norm,CRYPTO_1D_ENHANCED
+1,high_norm,CRYPTO_1D_ENHANCED
+2,low_norm,CRYPTO_1D_ENHANCED
+3,close_norm,CRYPTO_1D_ENHANCED
+4,volume_norm,CRYPTO_1D_ENHANCED
+5,sma_ratio_7,CRYPTO_1D_ENHANCED
+6,sma_ratio_14,CRYPTO_1D_ENHANCED
+7,sma_cross,CRYPTO_1D_ENHANCED
+8,ema_ratio_7,CRYPTO_1D_ENHANCED
+9,ema_ratio_14,CRYPTO_1D_ENHANCED
+10,macd_norm,CRYPTO_1D_ENHANCED
+11,rsi_14,CRYPTO_1D_ENHANCED
+12,rsi_7,CRYPTO_1D_ENHANCED
+13,roc_7,CRYPTO_1D_ENHANCED
+14,roc_14,CRYPTO_1D_ENHANCED
+15,momentum_7,CRYPTO_1D_ENHANCED
+16,bb_position,CRYPTO_1D_ENHANCED
+17,bb_width,CRYPTO_1D_ENHANCED
+18,atr_norm,CRYPTO_1D_ENHANCED
+19,volatility_7,CRYPTO_1D_ENHANCED
+20,volatility_14,CRYPTO_1D_ENHANCED
+21,volatility_30,CRYPTO_1D_ENHANCED
+22,obv_norm,CRYPTO_1D_ENHANCED
+23,vpt_norm,CRYPTO_1D_ENHANCED
+24,volume_change,CRYPTO_1D_ENHANCED
+25,volume_trend,CRYPTO_1D_ENHANCED
+26,upper_shadow,CRYPTO_1D_ENHANCED
+27,lower_shadow,CRYPTO_1D_ENHANCED
+28,body_ratio,CRYPTO_1D_ENHANCED
+29,daily_range,CRYPTO_1D_ENHANCED
+30,return_lag_1,CRYPTO_1D_ENHANCED
+31,return_lag_3,CRYPTO_1D_ENHANCED
+32,return_lag_7,CRYPTO_1D_ENHANCED
+33,return_lag_14,CRYPTO_1D_ENHANCED
+34,return_lag_30,CRYPTO_1D_ENHANCED

hf_cryptogat_crypto_1d/metadata/manifest.json

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+{
+  "hf_repo_id": "CharlieYPeng/cryptogat-crypto-1d",
+  "paper": {
+    "title": "CryptoGAT: Are Time Series Models Effective for Cryptocurrency Forecasting?",
+    "arxiv": "2606.27670",
+    "url": "https://arxiv.org/abs/2606.27670"
+  },
+  "github": "https://github.com/FanBroWell/CryptoGAT",
+  "raw_csv_files": 68,
+  "raw_rows": 68000,
+  "model_assets": 66,
+  "model_rows": 66000,
+  "excluded_from_model": [
+    "TUSDUSDT",
+    "USDCUSDT"
+  ],
+  "processed": {
+    "CRYPTO_1D_ALL": {
+      "eod_data.pkl": {
+        "shape": [
+          66,
+          999,
+          5
+        ],
+        "dtype": "float32"
+      },
+      "price_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "gt_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "mask_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "coin_names.txt": {
+        "count": 66,
+        "description": "Base asset symbols aligned to axis 0 of the processed arrays."
+      }
+    },
+    "CRYPTO_1D_ENHANCED": {
+      "eod_data.pkl": {
+        "shape": [
+          66,
+          999,
+          35
+        ],
+        "dtype": "float32"
+      },
+      "price_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "gt_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "mask_data.pkl": {
+        "shape": [
+          66,
+          999
+        ],
+        "dtype": "float32"
+      },
+      "coin_names.txt": {
+        "count": 66,
+        "description": "Base asset symbols aligned to axis 0 of the processed arrays."
+      }
+    }
+  }
+}

hf_cryptogat_crypto_1d/processed/CRYPTO_1D_ALL/coin_names.txt

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+1INCH
+AAVE
+ADA
+ALGO
+APT
+ARB
+AR
+ATOM
+AVAX
+BAT
+BCH
+BNB
+BTC
+CAKE
+CFX
+CHZ
+COMP
+CRV
+DASH
+DCR
+DOGE
+DOT
+ENS
+ETC
+ETH
+FET
+FIL
+GALA
+GLM
+GNO
+GRT
+HBAR
+ICP
+IMX
+INJ
+IOTA
+JASMY
+JST
+LDO
+LINK
+LTC
+LUNC
+MANA
+NEAR
+NEO
+NEXO
+OP
+PAXG
+QNT
+RAY
+RUNE
+SAND
+SHIB
+SOL
+STX
+SUN
+THETA
+TRX
+TWT
+UNI
+VET
+XEC
+XLM
+XRP
+XTZ
+ZEC

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+size 1318845

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hf_cryptogat_crypto_1d/processed/CRYPTO_1D_ENHANCED/coin_names.txt

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+1INCH
+AAVE
+ADA
+ALGO
+APT
+ARB
+AR
+ATOM
+AVAX
+BAT
+BCH
+BNB
+BTC
+CAKE
+CFX
+CHZ
+COMP
+CRV
+DASH
+DCR
+DOGE
+DOT
+ENS
+ETC
+ETH
+FET
+FIL
+GALA
+GLM
+GNO
+GRT
+HBAR
+ICP
+IMX
+INJ
+IOTA
+JASMY
+JST
+LDO
+LINK
+LTC
+LUNC
+MANA
+NEAR
+NEO
+NEXO
+OP
+PAXG
+QNT
+RAY
+RUNE
+SAND
+SHIB
+SOL
+STX
+SUN
+THETA
+TRX
+TWT
+UNI
+VET
+XEC
+XLM
+XRP
+XTZ
+ZEC

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